Instructions to use CatSnowdrop/universal_onnxruntime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use CatSnowdrop/universal_onnxruntime with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
universal_onnxruntime / onnxruntime /transformers /models /stable_diffusion /benchmark_controlnet.py
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Verified signature--- license: mit tags: - onnxruntime - onnx - inference - cuda - tensorrt - openvino - directml - windows --- # Universal ONNX Runtime (UORT) 1.30.1 — Windows x64 package folder A self-contained build of **ONNX Runtime 1.30.1** for **Windows x64 / CPython 3.12** with five execution providers compiled in and every native dependency bundled next to the Python extension: **TensorRT, CUDA, OpenVINO, DirectML and CPU**. This repository publishes the package as a **plain directory tree** — `onnxruntime/` at the repo root, exactly as it has to appear in `Lib\site-packages`. There is no archive to unpack: what you download is already the installable folder. The point of the package is that you install it by copying one folder — no CUDA Toolkit, no cuDNN download, no TensorRT SDK, no `pip install onnxruntime-gpu`. Everything the providers need is in `capi/`, and the package registers that directory with the Windows loader on import. | | | |---|---| | Contents | `onnxruntime…610d9c2